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Title:

Panel Regression with Random Noise

Description:

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we find estimates for the asymptotic variances of the...

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we find estimates for the asymptotic variances of these estimators. The paper focuses on multiplicative errors, which are often deliberately added to the data in order to minimize their disclosure risk. They can be analyzed in a similar way as additive errors, but with some important and consequential differences. ; panel regression, multiplicative measurement errors, bias correction, asymptotic variance, disclosure control Minimize

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preprint

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Title:

Some recent advances in measurement error models and methods

Description:

Measurement errors, error in variables, misclassification, efficiency comparison, survival analysis. JEL C13, C20, C24, C25

Measurement errors, error in variables, misclassification, efficiency comparison, survival analysis. JEL C13, C20, C24, C25 Minimize

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article

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Title:

The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

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Microaggregation is a set of procedures that distort empirical data in order to guarantee their facutal anonymity. At the same time the information content of data sets should not be reduced too much and should still be useful for scientific research. This paper investigates the effect of mircoaggregation on the estimation of a linear regression...

Microaggregation is a set of procedures that distort empirical data in order to guarantee their facutal anonymity. At the same time the information content of data sets should not be reduced too much and should still be useful for scientific research. This paper investigates the effect of mircoaggregation on the estimation of a linear regression by ordinary least squares. It studies, by way of an extensive simulation experiment, the bias of the slope parameter estimator induced by various microaggregation techniques. Some microaggregation procedures lead to consistent estimates while others imply an asymptotic bias for the estimator. ; Microaggregation, disclosure control, simple linear model, bias, consistency Minimize

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article

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Title:

Quasi Score is more Efficient than Corrected Score in a Polynomial Measurement Error Model

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Quasi score, Corrected score, Polynomial model, Measurement errors, Efficiency, Structural methods, Functional methods

Quasi score, Corrected score, Polynomial model, Measurement errors, Efficiency, Structural methods, Functional methods Minimize

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article

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Title:

Estimation of a linear regression under microaggregation with the response variable as a sorting variable

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article

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Title:

A small sample estimator for a polynomial regression with errors in the variables

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article

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Title:

Resolving the Ellsberg Paradox by Assuming that People Evaluate Repetitive Sampling

Description:

this paper, their behavior is irrational in that they misconceive the important, albeit a bit artificial, trait of the experiment that only one draw is to be executed. In the first interpretation people show ambiguity aversion in the second they show risk aversion.

this paper, their behavior is irrational in that they misconceive the important, albeit a bit artificial, trait of the experiment that only one draw is to be executed. In the first interpretation people show ambiguity aversion in the second they show risk aversion. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper153.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper153.ps.Z Minimize

Document Type:

text

Language:

en

Subjects:

Key words ; Ellsberg's paradox ; rational decision making ; Sure Thing Principle ; subjective probabilities

Key words ; Ellsberg's paradox ; rational decision making ; Sure Thing Principle ; subjective probabilities Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Abraham Wald

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This paper grew out of a lecture presented at the 54 th Session of the International Statistical Institute in Berlin, August 13- 20, 2003, Schneeweiss (2003). It intends not only to outline the eventful life of Abraham Wald (1902- 1950) in Austria and in the United States but also to present his extensive scientific work. In particular, the two ...

This paper grew out of a lecture presented at the 54 th Session of the International Statistical Institute in Berlin, August 13- 20, 2003, Schneeweiss (2003). It intends not only to outline the eventful life of Abraham Wald (1902- 1950) in Austria and in the United States but also to present his extensive scientific work. In particular, the two main subjects, where he earned most of his fame, are outline: Statis-tical Decision Theory and Sequential Analysis. In addition, emphasis is laid on his contributions to Econometrics and related fields. Abraham Wald is best known, indeed he is famous, for having founded Statistical Decision Theory and also for having developed the theory of se-quential sampling. But he also contributed to many other fields of Statistics often giving decisive impulses or even originating new directions of research. In Statistics proper one might mention: asymptotic maximum likelihood theory, nonparametric statistics, tolerance intervals, optimal experimental Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-06-16

Source:

http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper439.pdf

http://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper439.pdf Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Estimating A Polynomial Regression With Measurement Errors In The Structural And In The Functional Case -- A Comparison

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-09-16

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper197.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper197.ps.Z Minimize

Document Type:

text

Language:

en

Subjects:

Polynomial regression ; measurement errors ; efficiency ; robustness

Polynomial regression ; measurement errors ; efficiency ; robustness Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Some recent advances in measurement error models and methods

Description:

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robus...

A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators. Several methods have been proposed to eliminate, or at least to reduce, this bias, and the relative efficiency and robustness of these methods have been compared. The paper gives an account of these endeavors. In another context, when data are of a categorical nature, classification errors play a similar role as measurement errors in continuous data. The paper also reviews some recent advances in this field. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2010-09-16

Source:

http://epub.ub.uni-muenchen.de/1821/1/paper_452.pdf

http://epub.ub.uni-muenchen.de/1821/1/paper_452.pdf Minimize

Document Type:

text

Language:

en

Subjects:

error in variables ; misclassification ; efficiency comparison ; survival analysis ; JEL C13 ; C20 ; C24 ; C25

error in variables ; misclassification ; efficiency comparison ; survival analysis ; JEL C13 ; C20 ; C24 ; C25 Minimize

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